References
- Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of Icnn95 – International Conference on Neural Networks, 1942–1948. Piscataway, NJ: IEEE Press; 1995.
- Tang KS, Man KF, Kwong S, et al. Genetic algorithms and their applications. IEEE Signal Process Mag. 1996;13(6):22–37.
- Storn R, Price K. Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim. 1997;11(4):341–359.
- Yang X, Hosseini S, Gandomi AH. Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput. 2012;12(3):1180–1186.
- Solimanpur M, Vrat P, Shankar R. Ant colony optimization algorithm to the inter-cell layout problem in cellular manufacturing. Eur J Oper Res. 2004;157(3):592–606.
- Pan W. A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge Based Syst. 2012;26:69–74.
- Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Softw. 2014;69:46–61.
- Mirjalili S, Lewis A. The whale optimization algorithm. Adv Eng Softw. 2016;95:51–67.
- Yao X, Liu Y, Lin G. Evolutionary programming made faster. IEEE Trans Evol Comput. 1999;3(2):82–102.
- Hansen N, Müller SD, Koumoutsakos P. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol Comput. 2003;11(1):1–18.
- Bentouati B, Chaib L, Chettih S. A hybrid whale algorithm and pattern search technique for optimal power flow problem. 8th International Conference on Modelling, Identification and Control (ICMIC-2016). Algiers, Algeria: IEEE Press; 2016. p. 1048–1053.
- Aljarah I, Faris H, Mirjalili S. Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput. 2018;22(1):1–15.
- Aziz M, Ewees AA, Hassanien AE. Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Syst Appl. 2017;83:242–256.
- Ismail SG, Ashraf D, Ella HA. A new chaotic whale optimization algorithm for features selection. J Class. 2018;35:300–344.
- Zhou Y, Xiang Y, Chen Z, et al. A scalar projection and angle-based evolutionary algorithm for many-objective optimization problems. IEEE Trans Cybern. 2019;49(6):2073–2084.
- Kao Y, Zahara E. A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Appl Soft Comput J. 2008;8(2):849–857.
- Rahmani A, MirHassani SA. A hybrid Firefly – genetic algorithm for the capacitated facility location problem. Inform Sci. 2014;283:70–78. https://doi.org/10.1016/j.ins.2014.06.002.
- Gong Y, Li J, Zhou Y, et al. Genetic learning particle swarm optimization. IEEE Trans Cybern. 2016;46(10):2277–2290.
- Yuce B, Fruggiero F, Packianather MS, et al. Hybrid genetic bees algorithm applied to single machine scheduling with earliness and tardiness penalties. Comput Indus Eng. 2017;113:842–858.
- Xue B, Zhang M, Browne WN. Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans Cybern. 2013;43(6):1656–1671.
- Omran M, Salman A, Engelbrecht AP. Dynamic clustering using particle swarm optimization with application in image segmentation. Pattern Anal Appl. 2006;8(4):332–344.
- Niu Q, Jiao B, Gu X. Particle swarm optimization combined with genetic operators for job shop scheduling problem with fuzzy processing time. Appl Math Comput. 2008;205(1):148–158.
- Li D, Chen S, Huang H. Improved genetic algorithm with two-level approximation for truss topology optimization. Struct Multidisc Optim. 2014;49(5):795–814.
- Liu Z, Li H, Zhu P. Diversity enhanced particle swarm optimization algorithm and its application in vehicle lightweight design. J Mech Sci Technol. 2019;33(2):695–709.
- Fesanghary M, Mahdav M, Minary-Jolandan M, et al. Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Comput Methods Appl Mech Eng. 2008;197(33–40):3080–3091.
- Li Y, Zhan Z, Li S, et al. Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems. Inform Sci. 2015;293:370–382.
- Chou JS, Ngo NT. Modified firefly algorithm for multidimensional optimization in structural design problems. Struct Multidisc Optim. 2017;55(6):2013–2028.
- Too J, Abdullah AR. A new and fast rival genetic algorithm for feature selection. J Supercomput. 2021;77(3):2844–2874.
- Yuan X, Dai X, Zhao J, et al. On a novel multi-swarm fruit fly optimization algorithm and its application. Appl Math Comput. 2014;233(3):260–271.
- Zhang Y, Cui G, Wu J, et al. A novel multi-scale cooperative mutation fruit fly optimization algorithm. Knowl Based Syst. 2016;114(15):24–35.
- Zhou Y, Ling Y, Luo Q. Lévy flight trajectory-based whale optimization algorithm for global optimization. IEEE Access. 2017;5:6168–6186.
- Wolpert DH, Macready WG. No free lunch theorems for optimization. IEEE Trans Evol Comput. 1997;1(1):67–82.
- Juang CF. A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans Syst Man Cybern B Cybern. 2004;34(2):997–1006.
- Ahn CW, Kim E, Kim HT, et al. A hybrid multi-objective evolutionary algorithm: striking a balance with local search. Math Comput Model. 2010;52(11–12):2048–2059.
- Oliva D, Aziz M, Hassanien AE. Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Appl Energy. 2017;200:141–154.
- Kaur G, Arora S. Chaotic whale optimization algorithm. J Comput Des Eng. 2018;5(3):275–284.
- Mafarja MM, Mirjalili S. Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing. 2017;260:302–312.
- Tubishat M, Abushariah M, Idris N, et al. Improved whale optimization algorithm for feature selection in Arabic sentiment analysis. Appl Intell. 2019;49(5):1688–1707.
- Luo J, Shi B. A hybrid whale optimization algorithm based on modified differential evolution for global optimization problems. Appl Intell. 2019;49(5):1982–2000.
- Deb K. An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng. 2000;186(2–4):311–338.
- Agrawal RB, Deb K, Agrawal RB. Simulated binary crossover for continuous search space. Complex Syst. 2000;9(3):115–148.
- Deb K, Deb D. Analysing mutation schemes for real-parameter genetic algorithms. IJAISC. 2014;4(1):1–28.
- Zeng G, Chen J, Li L, et al. An improved multi-objective population-based extremal optimization algorithm with polynomial mutation. Inform Sci. 2016;330:49–73.
- Coello C, Lechuga MS. MOPSO: a proposal for multiple objective particle swarm optimization. WCCI IEEE Comput Soc. 2002;2:1051–1056.
- Qu B, Suganthan PN. Constrained multi-objective optimization algorithm with an ensemble of constraint handling methods. Eng Optim. 2011;43(4):403–416.
- Deb K, Pratap A, Agarwal S, et al. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput. 2002;6(2):182–197.
- Coello C, Pulido GT, Lechuga MS. Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput. 2004;8(3):256–279.
- Zhang Q, Li H. MOEA/D: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput. 2008;11(6):712–731.
- Wang L, Yang Y, Dong C, et al. Multi-objective optimization of coal-fired power plants using differential evolution. Appl Energy. 2014;115:254–264.
- Zitzler E, Thiele L, Laumanns M, et al. Performance assessment of multi-objective optimizers: an analysis and review. IEEE Trans Evol Comput. 2003;7(2):117–132.
- Li Y, Shao X, Cai W. A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples. Talanta. 2007;72(1):217–222.
- Gu X, Lu J. Reliability-based robust assessment for multiobjective optimization design of improving occupant restraint system performance. Comput Indus. 2014;65(8):1169–1180.